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https://issues.apache.org/jira/browse/MAHOUT-1419?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andrew Musselman updated MAHOUT-1419:
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    Attachment:     (was: run-rf.sh)

> Random decision forest is excessively slow on numeric features
> --------------------------------------------------------------
>
>                 Key: MAHOUT-1419
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1419
>             Project: Mahout
>          Issue Type: Bug
>          Components: Classification
>    Affects Versions: 0.7, 0.8, 0.9
>            Reporter: Sean Owen
>         Attachments: MAHOUT-1419.patch, create-rf-data.sh, run-rf.sh
>
>
> Follow-up to MAHOUT-1417. There's a customer running this and observing it 
> take an unreasonably long time on about 2GB of data -- like, >24 hours when 
> other RDF M/R implementations take 9 minutes. The difference is big enough to 
> probably be considered a defect. MAHOUT-1417 got that down to about 5 hours. 
> I am trying to further improve it.
> One key issue seems to be how splits are evaluated over numeric features. A 
> split is tried for every distinct numeric value of the feature in the whole 
> data set. Since these are floating point values, they could (and in the 
> customer's case are) all distinct. 200K rows means 200K splits to evaluate 
> every time a node is built on the feature.
> A better approach is to sample percentiles out of the feature and evaluate 
> only those as splits. Really doing that efficiently would require a lot of 
> rewrite. However, there are some modest changes possible which get some of 
> the benefit, and appear to make it run about 3x faster. That is --on a data 
> set that exhibits this problem -- meaning one using numeric features which 
> are generally distinct. Which is not exotic.
> There are comparable but different problems with handling of categorical 
> features, but that's for a different patch.
> I have a patch, but it changes behavior to some extent since it is evaluating 
> only a sample of splits instead of every single possible one. In particular 
> it makes the output of "OptIgSplit" no longer match the "DefaultIgSplit". 
> Although I think the point is that "optimized" may mean giving different 
> choices of split here, which could yield differing trees. So that test 
> probably has to go.
> (Along the way I found a number of micro-optimizations in this part of the 
> code that added up to maybe a 3% speedup. And fixed an NPE too.)
> I will propose a patch shortly with all of this for thoughts.



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